CE314-6-AU-CO:
Natural Language Engineering

The details
2023/24
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Undergraduate: Level 6
Current
Thursday 05 October 2023
Friday 15 December 2023
15
16 June 2022

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (Including foundation Year),
BSC LL20 Politics with Data Science,
BSC LL21 Politics with Data Science,
BSC LL22 Politics with Data Science,
BSC I400 Artificial Intelligence,
BSC I401 Artificial Intelligence (Including Foundation Year),
BSC I402 Artificial Intelligence (including Placement Year),
BSC I403 Artificial Intelligence (including Year Abroad)

Module description

As humans we are adept in understanding the meaning of texts and conversations. We can also perform tasks such as summarize a set of documents to focus on key information, answer questions based on a text, and when bilingual, translate a text from one language into fluent text in another language. Natural Language Engineering (NLE) aims to create computer programs that perform language tasks with similar proficiency.

This course provides a strong foundation to understand the fundamental problems in NLE and also equips students with the practical skills to build small-scale NLE systems. Students are introduced to three core ideas of NLE: a) gaining an understanding the core elements of language--- the structure and grammar of words, sentences and full documents, and how NLE problems are related to defining and learning such structures, b) identify the computational complexity that naturally exists in language tasks and the unique problems that humans easily solve but are incredibly hard for computers to do, and c) gain expertise in developing intelligent computing techniques which can overcome these challenges.

Module aims

The aim of this module is to introduce key ideas and techniques used in the design and implementation of natural language engineering applications. We will primarily cover statistical methods, and will look at the use of such methods in applications.

Module learning outcomes

After completing this module, students will be expected to be able to:

1. Describe and formalize how language problems can be solved computationally.
2. Understand and implement techniques for language modelling, speech tagging, and syntactic parsing.
3. Understand and implement techniques for computational semantics and discourse processing.
4. Understand, implement and use algorithms such as Viterbi decoding, and basic supervised classification.
5. Understand how NLE techniques can be used to design and implement applications such as text summarization, sentiment analysis and writing quality prediction.

Module information

Outline Syllabus

Language models
Topic classification
Part-of-speech tagging
Syntactic parsing
Lexical semantics
Discourse processing
NLE applications such as text summarization, sentiment analysis, and identifying writing quality

Learning and teaching methods

Lectures and Labs/Classes

Bibliography

The above list is indicative of the essential reading for the course.
The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students.
Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Assignment 1 - Practical exercise 1     33.34% 
Coursework   Assignment 2 - Practical exercise 2     66.66% 
Exam  Main exam: In-Person, Open Book (Restricted), 120 minutes during Early Exams 
Exam  Reassessment Main exam: In-Person, Open Book (Restricted), 120 minutes during September (Reassessment Period) 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
30% 70%

Reassessment

Coursework Exam
30% 70%
Module supervisor and teaching staff
Dr Yunfei Long, email: yl20051@essex.ac.uk.
Dr Yunfei Long
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770

 

Availability
Yes
No
Yes

External examiner

Prof Pietro Oliveto
Southern University of Science and Technology (SUSTech)
Professor
Resources
Available via Moodle
Of 59 hours, 47 (79.7%) hours available to students:
12 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information

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